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Texture Measurement Through Local Pattern Quantization for SAR Image Classification

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Abstract

A novel local pattern based classification algorithm for SAR image is proposed in this paper. The proposed method initially quantizes homogeneous and non-homogeneous patterns within the moving window. An operator is constructed to quantize local patterns. Quantized patterns are then used for measuring texture around the central pixel within the moving window. The ISODATA algorithm is used to classify texture transformed image. The proposed classification method is robust to speckle noise, computationally simple and does not need to set any predefined parameter for classification. The validation of the method is done on RISAT-1 and RISAT-2 data.

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Acknowledgments

Authors sincerely thank Dr. V.K. Dadhwal, Director, NRSC, Hyderabad, India for his continuous support and guidance during the course of this study.

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Authors and Affiliations

  1. RRSC-East (NRSC, ISRO), New Town, Kolkata, 700 156, India

    Debasish Chakraborty & Dibyendu Dutta

  2. Regional Centres (NRSC, ISRO), Balanagar, Hyderabad, 500 037, India

    Jaswant Raj Sharma

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  1. Debasish Chakraborty
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  2. Dibyendu Dutta
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  3. Jaswant Raj Sharma
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Correspondence to Debasish Chakraborty.

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Chakraborty, D., Dutta, D. & Sharma, J.R. Texture Measurement Through Local Pattern Quantization for SAR Image Classification. J Indian Soc Remote Sens 44, 471–477 (2016). https://doi.org/10.1007/s12524-015-0495-8

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  • DOI: https://doi.org/10.1007/s12524-015-0495-8

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